Technical Field
The present disclosure relates to computerized data processing and search technologies. More particularly, and without limitation, the present disclosure relates to systems and methods for identifying search query input scenarios, and techniques for classifying terms and/or phrases of search queries based on known input scenarios.
Background
Use of the Internet has grown significantly in recent years. Internet access is now available from a variety of devices, such as personal computers, laptops, tablets, personal digital assistants (PDAs), mobile phones, smart-phones, televisions, and other devices. With the increased access to the Internet from a wide variety of devices, people have become more reliant than ever on online search engines to submit queries and find desired information.
Web sites offer a variety of different search engines for finding desired information from a large pool of available information. Both generalized search engines and specialized search engines are available. For example, Google™ and Bing™ provide web sites for conducting generalized web searches. Specialized search engines are available for searching within particular websites or content categories. For example, search engines are available for searching for news, products, jobs, events, entertainment, legal information, medical information, geographic or map information, recipes, friends, real estate and much more. There are also specialized search engines for searching for particular types of content. For example, search engines are available for searching for audio files, video files, local content, and other types of specific information or content.
Some search engines build searchable indexes of information from relational databases, which contain structured information. This information often contains metadata or is otherwise labeled. When searching against such structured information, it is beneficial to label a term or phrase in a search query, and to compare the label with the labels in the indexed information to obtain more relevant results. For example, a user entering a search query for the word “Washington” may receive search results relating to George Washington, when the user intended to search for information about Washington, D.C. However, if the term “Washington” in the search query were labeled as a city, the search engine could search indexed information for only “Washington” terms labeled as referring to the city.
There are a variety of different ways in which search engines allow users to enter queries. Some search engines provide separate fields or codes, allowing a user to designate a particular query term or phrase as relating to a particular type of information, and thereby associate the term or phrase with a label. For example, a bookseller may provide a search allowing a user to search through only book titles or author names for a particular term. However, requiring a user to select a field or code may overly restrict the scope of a search, or may confuse users.
Other search engines provide a field for entering a search query in a natural language format. These search engines may separate a search query into terms after the user has entered the search query, and may search for all combinations of the terms. However, such an approach is computationally intensive and error prone. Alternatively, search engines may attempt to identify types of terms in a natural language search based on comparisons with pre-stored terms in a dictionary or database. However, such an approach may introduce a large number of false positives for queries whose terms were not intended to have the same meaning as the corresponding terms in the dictionary. Accordingly, while it would be beneficial to label terms or phrases in search queries, current approaches are computationally intensive, error prone, restrictive, and/or confusing for users.